Significant research and development efforts are currently directed toward designing and implementing artificial synapses for neural network based learning systems, such as memristors and synaptic transistors. The memristor is a two-terminal passive device with a variable internal resistance. This resistance depends on the amount of charge which passed through the memristor by a bias applied before. As soon as the desired internal resistance is adjusted, this biasing is interrupted. The memristor will thus maintain exactly this internal resistance until the next biasing is applied. Recently, the memristor was discussed in literature in connection with synapses and neuro-morphological systems.
Memristors made of various materials and structures have been researched. Synaptic transistors made of nano batteries have also archived quite impressive results. However, the performance of these synaptic devices are not sufficient for practical neuromorphic computing. For example, memristors with metal-insulator-metal structures have unstable, uncontrollable, read/write noise resistive behaviors. Synaptic transistors decouple the electron and ion to reduce the read/write noise and have presented improved results, but the transistor structures have a scaling up problem since there are approximately 1.5×1014 synapses, connecting 19-23 billion neurons in human brain.
Synaptic plasticity, an ability for synapses to strengthen or weaken, is a fundamental mechanism how synapses learn and adapt over time. A synaptic device is an electronic switch which can simulate a biological synapse in both function and structure, and a synaptic device is essential for neuromorphic computing, including brain-like computing and brain-inspired computing. In biology, a synapse is used to convert electric signals to chemical signals in pre-synapses and reverse chemical signals into electric signals by post-synapses. Synapses have two terminal structures that permits a neuron to pass an electrical or chemical signal to another neuron. Nanobatteries can regulate the ionic concentration in anodes and cathodes by an external electric field through electrochemical reactions. However, to read out the conductance of the nanobattery in a two-terminal device is a formidable challenge because the electrolyte of the nanobattery is made of highly electrical resistive materials. To achieve a synaptic memristor, a suitable physical effect and novel structure are needed.